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The paper is a follow-up of [R.J.: Foundations of compositional model theory. IJGS, 40(2011): 623-678], where basic properties of compositional models, as one of the approaches to multidimensional probability distributions representation and processing, were introduced. In fact, it is an algebraic alternative to graphical models, which does not use graphs to represent conditional independence statements. Here, these statements are encoded in a sequence of distributions to which an operator of composition -the key element of this theory -is applied in order to assemble a multidimensional model from its low-dimensional parts. In this paper, we show a way to read conditional independence relations, and to solve related topics, above all the socalled equivalence problem, i.e. the problem of recognizing whether two different structures induce the same system of conditional independence relations.